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Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

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Pesticide effect on earthworm lethality via interpretable machine learning.

Mihkel Kotli¹, Geven Piir¹, Uko Maran¹

¹University of Tartu, Institute of Chemistry, Tartu, Estonia.

Journal of Hazardous Materials

|October 4, 2023

Summary

This study developed a quantitative structure-activity relationship (QSAR) model to predict pesticide toxicity in earthworms. The model helps assess chemical impacts on soil health, aiding regulatory decisions.

Keywords:

Bayesian optimization Earthworm acute toxicity Genetic algorithm QSAR Random forest

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Area of Science:

Environmental Toxicology
Ecotoxicology
Soil Science

Background:

Earthworms are crucial for soil health.
Pesticides used in agriculture can harm soil invertebrates.
Assessing chemical impacts is vital for regulation.

Purpose of the Study:

To establish a quantitative structure-activity relationship (QSAR) between pesticide chemical structures and acute toxicity in Eisenia fetida.
To develop a predictive model for evaluating chemical impacts on earthworms.

Main Methods:

Collected pesticide chemical data from open-access sources.
Employed a hybrid approach combining genetic algorithms and Bayesian optimization for feature selection and parameter tuning.
Utilized the Random Forest algorithm to build the final QSAR classification model.

Main Results:

Achieved a prediction accuracy of 0.78 on the training set and 0.80 on the test set.
The developed QSAR model effectively predicts acute toxicity in Eisenia fetida.
The model adheres to FAIR principles and is publicly available on QsarDB.org.

Conclusions:

A robust QSAR model was successfully developed to predict pesticide toxicity in earthworms.
This model facilitates better assessment of chemical risks to soil ecosystems.
The findings support informed decision-making for pesticide regulation and environmental safety.